
"""simple docstring"""






import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


UpperCAmelCase        =				{
    """169M""": 1_2,
    """430M""": 2_4,
    """1B5""": 2_4,
    """3B""": 3_2,
    """7B""": 3_2,
    """14B""": 4_0,
}

UpperCAmelCase        =				{
    """169M""": 7_6_8,
    """430M""": 1_0_2_4,
    """1B5""": 2_0_4_8,
    """3B""": 2_5_6_0,
    """7B""": 4_0_9_6,
    """14B""": 5_1_2_0,
}




def __magic_name__	(				_lowerCamelCase:		Optional[int]			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  list(state_dict.keys()			)
	for name in state_dict_keys:
		lowerCAmelCase												=  state_dict.pop(_lowerCamelCase			)
		# emb -> embedding
		if name.startswith('''emb.'''			):
			lowerCAmelCase												=  name.replace('''emb.''',					'''embeddings.'''			)
		# ln_0 -> pre_ln (only present at block 0)
		if name.startswith('''blocks.0.ln0'''			):
			lowerCAmelCase												=  name.replace('''blocks.0.ln0''',					'''blocks.0.pre_ln'''			)
		# att -> attention
		lowerCAmelCase												=  re.sub(r'''blocks\.(\d+)\.att''',					r'''blocks.\1.attention''',					_lowerCamelCase			)
		# ffn -> feed_forward
		lowerCAmelCase												=  re.sub(r'''blocks\.(\d+)\.ffn''',					r'''blocks.\1.feed_forward''',					_lowerCamelCase			)
		# time_mix_k -> time_mix_key and reshape
		if name.endswith('''.time_mix_k'''			):
			lowerCAmelCase												=  name.replace('''.time_mix_k''',					'''.time_mix_key'''			)
		# time_mix_v -> time_mix_value and reshape
		if name.endswith('''.time_mix_v'''			):
			lowerCAmelCase												=  name.replace('''.time_mix_v''',					'''.time_mix_value'''			)
		# time_mix_r -> time_mix_key and reshape
		if name.endswith('''.time_mix_r'''			):
			lowerCAmelCase												=  name.replace('''.time_mix_r''',					'''.time_mix_receptance'''			)

		if name != "head.weight":
			lowerCAmelCase												=  '''rwkv.''' + name

		lowerCAmelCase												=  weight
	return state_dict




def __magic_name__	(				_lowerCamelCase:		List[str],					_lowerCamelCase:		List[Any],					_lowerCamelCase:		Any,					_lowerCamelCase:		Union[str, Any]=None,					_lowerCamelCase:		Any=None,					_lowerCamelCase:		List[Any]=False,					_lowerCamelCase:		Optional[int]=None			)					->						str:

	'''simple docstring'''




	if tokenizer_file is None:
		print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''			)
		lowerCAmelCase												=  50_277
		lowerCAmelCase												=  AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''			)
	else:
		lowerCAmelCase												=  PreTrainedTokenizerFast(tokenizer_file=_lowerCamelCase			)
		lowerCAmelCase												=  len(_lowerCamelCase			)
	tokenizer.save_pretrained(_lowerCamelCase			)

	# 2. Build the config
	lowerCAmelCase												=  list(NUM_HIDDEN_LAYERS_MAPPING.keys()			)
	if size is None:
		# Try to infer size from the checkpoint name
		for candidate in possible_sizes:
			if candidate in checkpoint_file:
				lowerCAmelCase												=  candidate
				break
		if size is None:
			raise ValueError('''Could not infer the size, please provide it with the `--size` argument.'''			)
	if size not in possible_sizes:
		raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}."""			)

	lowerCAmelCase												=  RwkvConfig(
	    vocab_size=_lowerCamelCase,					num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size],					hidden_size=HIDEN_SIZE_MAPPING[size],					)
	config.save_pretrained(_lowerCamelCase			)

	# 3. Download model file then convert state_dict
	lowerCAmelCase												=  hf_hub_download(_lowerCamelCase,					_lowerCamelCase			)
	lowerCAmelCase												=  torch.load(_lowerCamelCase,					map_location='''cpu'''			)
	lowerCAmelCase												=  convert_state_dict(_lowerCamelCase			)

	# 4. Split in shards and save
	lowerCAmelCase							,       lowerCAmelCase												=  shard_checkpoint(_lowerCamelCase			)
	for shard_file, shard in shards.items():
		torch.save(_lowerCamelCase,					os.path.join(_lowerCamelCase,					_lowerCamelCase			)			)

	if index is not None:
		lowerCAmelCase												=  os.path.join(_lowerCamelCase,					_lowerCamelCase			)
		# Save the index as well
		with open(_lowerCamelCase,					'''w''',					encoding='''utf-8'''			) as f:
			lowerCAmelCase												=  json.dumps(_lowerCamelCase,					indent=2,					sort_keys=_lowerCamelCase			) + '''\n'''
			f.write(_lowerCamelCase			)

		# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
		print(
		    '''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.'''			)
		lowerCAmelCase												=  list(shards.keys()			)

		del state_dict
		del shards
		gc.collect()

		for shard_file in shard_files:
			lowerCAmelCase												=  torch.load(os.path.join(_lowerCamelCase,					_lowerCamelCase			)			)
			torch.save({k: v.cpu().clone() for k, v in state_dict.items()},					os.path.join(_lowerCamelCase,					_lowerCamelCase			)			)

	del state_dict
	gc.collect()

	if push_to_hub:
		if model_name is None:
			raise ValueError('''Please provide a `model_name` to push the model to the Hub.'''			)
		lowerCAmelCase												=  AutoModelForCausalLM.from_pretrained(_lowerCamelCase			)
		model.push_to_hub(_lowerCamelCase,					max_shard_size='''2GB'''			)
		tokenizer.push_to_hub(_lowerCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
					)
					parser.add_argument(
					    """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
					)
					parser.add_argument(
					    """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
					)
					parser.add_argument(
					    """--tokenizer_file""",
					    default=None,
					    type=str,
					    help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
					)
					parser.add_argument(
					    """--size""",
					    default=None,
					    type=str,
					    help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					    help="""Push to the Hub the converted model.""",
					)
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help="""Name of the pushed model on the Hub, including the username / organization.""",
					)

					UpperCAmelCase        =				parser.parse_args()
					convert_rmkv_checkpoint_to_hf_format(
					    args.repo_id,
					    args.checkpoint_file,
					    args.output_dir,
					    size=args.size,
					    tokenizer_file=args.tokenizer_file,
					    push_to_hub=args.push_to_hub,
					    model_name=args.model_name,
					)




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}



class 			lowercase					( lowercase__   ,lowercase__		):
			lowercase        =     '''nat'''

			lowercase        =     {
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=4      ,SCREAMING_SNAKE_CASE_  :	Dict=3      ,SCREAMING_SNAKE_CASE_  :	int=64      ,SCREAMING_SNAKE_CASE_  :	Tuple=[3, 4, 6, 5]      ,SCREAMING_SNAKE_CASE_  :	Dict=[2, 4, 8, 16]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=7      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=3.0      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=True      ,SCREAMING_SNAKE_CASE_  :	Any=0.0      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	Tuple="gelu"      ,SCREAMING_SNAKE_CASE_  :	str=0.02      ,SCREAMING_SNAKE_CASE_  :	str=1e-5      ,SCREAMING_SNAKE_CASE_  :	int=0.0      ,SCREAMING_SNAKE_CASE_  :	List[Any]=None      ,SCREAMING_SNAKE_CASE_  :	Tuple=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,)       ->	Tuple:





				"""simple docstring"""


				super().__init__(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  patch_size
				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  embed_dim
				lowerCAmelCase												=  depths
				lowerCAmelCase												=  len(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  num_heads
				lowerCAmelCase												=  kernel_size
				lowerCAmelCase												=  mlp_ratio
				lowerCAmelCase												=  qkv_bias
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  drop_path_rate
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  layer_norm_eps
				lowerCAmelCase												=  initializer_range
				# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				lowerCAmelCase												=  int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_			) - 1)			)
				lowerCAmelCase												=  layer_scale_init_value
				lowerCAmelCase												=  ['''stem'''] + [F"""stage{idx}""" for idx in range(1      ,len(SCREAMING_SNAKE_CASE_			) + 1			)]
				lowerCAmelCase							,       lowerCAmelCase												=  get_aligned_output_features_output_indices(
				    out_features=SCREAMING_SNAKE_CASE_      ,out_indices=SCREAMING_SNAKE_CASE_      ,stage_names=self.stage_names			)


